Neuromorphic (codenamed Loihi) is a new computing approach inspired by how brain functions, which will make machine learning more effective.

Intel’s roadmap suggests they could achieve 1,000-qubit system within 5-7 years.

Intel made two major announcements at 2018 Consumer Electronics Show (CES) which have the potential to help customers and solve problem that are far beyond conventional computers’ reach. Intel CEO, Brian Krzanich, showed two processors – the one advances quantum computing, while the other deals with neuromorphic computing.

He showed the progress made in neuromorphic computing processor, which is a self learning chip, codenamed “Loihi” that mimics the brain’s basic operation in order to make machine learning more efficient.

The other processor is 49-qubit superconducting quantum test chip, codenamed “Tangle Lake”. If you don’t recognize, it’s named after a chain of lakes in Alaska, that implies quantum processors require extreme cold temperatures to function.

Intel is trying to drive the exponentially increasing demand of computing performance with new specialized architectures. Let’s elaborate these two architectures and related research.

Loihi – Processor That Mimics Brain

Intel’s CEO showcased the research company has done in neuromorphic computing – a new computing approach inspired by how brain functions. In order to make artificial intelligence and machine learning more efficient Loihi merges training and inference on a single processor.

Just like animal/human brain, the Loihi connects up neurons to get together and it changes the connectivity between neurons. What Loihi gives you is the capabilities to develop a network, feed in data and then it’ll change the network as you proceed and learn, and it will tell you what the result is – online, at the edge, consuming low power.

Features

Completely asynchronous neuromorphic mesh supports different hierarchical, sparse and recurrent neural network topologies, where each neuron can communicate with thousands of other neurons.

Each core has a learning engine which could be programmed to dynamically adapt network parameters, supporting reinforcement, supervised and unsupervised learning approaches.

A total of 130,000 neurons and 130 million synapses fabricated on 14 nanometer process technology.

Development and testing of numerous algorithm with high efficiency for problems, like constraint satisfaction, dynamic pattern learning, path planning, dictionary learning, sparse coding and more.

Self-learning neuromorphic research chip

The processor could be used anywhere real world information needs to be handled in ever-growing realtime environments. For instance, it will be capable enough to enable smarter security cameras, augmented/mixed reality devices and autonomous vehicles communicating in realtime.

As of now, Intel has fully functioning neuromorphic research processor and they aim to share the prototype with leading research institutions (while applying it to more complicated problems and data structures) by the end of the 2nd quarter of 2018.

Tangle Lake – Superconducting Quantum Test Chip

In October 2017, Intel delivered a 17-qubit superconducting chip with advanced packaging, and now, just 3 months later, they have unveiled a 49-qubit quantum test chip that represents the impressive progress towards their objective of developing a fully functional computing system – from control electronics to algorithms.

It’ll likely require one million or more qubits to achieve commercial relevance. The quantum computing will solve problems that typically take today’s most powerful supercomputers months to resolve, like financial modeling, drug development and climate forecasting.

In the last couple of years, the race of building quantum computers among the tech giants has gotten pretty interesting. In November 2017, IBM scientists stated that they had developed a 50-qubit quantum chip prototype. In the same year, Google talked about its goals to build a 49-qubit superconducting quantum chip. But it is still going to be a long way before they develop a full-scale quantum machine. Intel’s roadmap suggests they could achieve 1,000 qubit system within 5-7 years.

Intel is also investing big in researching spin qubits in silicon. Since they’re much smaller than superconducting qubits, they could’ve a scaling benefit. Basically, spin qubits resemble a single electron transistor -= similar to traditional transistors — that could be manufactured with comparable techniques. So far, Intel has developed a spin qubit fabrication flow on 300 millimeter process technology.

Neither neuromorphic nor quantum computing is going to replace general purpose computers, but they are supposed to enhance it.